Discovering Tampered Image in Social Media Using ELA and Deep Learning

被引:0
|
作者
Chakraborty S. [1 ]
Chatterjee K. [2 ]
Dey P. [1 ]
机构
[1] Department of Information Technology, Government College of Engineering & Ceramic Technology, Kolkata
[2] Department of Computer Science & Engineering, Government College of Engineering & Ceramic Technology, Kolkata
关键词
Convolutional neural networks; Deep learning; Error level analysis; Image tampering;
D O I
10.1007/s42979-022-01311-w
中图分类号
学科分类号
摘要
In the era of social media, we have access to millions of images. Nowadays with the rise of many advanced photo editing software finding a tampered image online is a very common situation. Most of the time an image is tampered for fun, but there are scenarios where an image is tampered with malicious intent and can cause harm to society. Digital image forensics is having a tough time dealing with tampered images due to the advancement of technology. Here, in our approach, we combined error level analysis (ELA) with a convolutional neural network (CNN) to classify whether an image is authentic or not. Our experiment has yielded a validation accuracy of 96.18% after 24 epochs. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
引用
收藏
相关论文
共 50 条
  • [1] Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media
    Abousaleh, Fatma S.
    Cheng, Wen-Huang
    Yu, Neng-Hao
    Tsao, Yu
    IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2021, 13 (03) : 679 - 692
  • [2] Detection of Image Tampering Using Deep Learning, Error Levels and Noise Residuals
    Chakraborty, Sunen
    Chatterjee, Kingshuk
    Dey, Paramita
    NEURAL PROCESSING LETTERS, 2024, 56 (02)
  • [3] Detection of Image Tampering Using Deep Learning, Error Levels and Noise Residuals
    Sunen Chakraborty
    Kingshuk Chatterjee
    Paramita Dey
    Neural Processing Letters, 56
  • [4] Exploration of social media for sentiment analysis using deep learning
    Liang-Chu Chen
    Chia-Meng Lee
    Mu-Yen Chen
    Soft Computing, 2020, 24 : 8187 - 8197
  • [5] Filtering Relevant Comments in Social Media Using Deep Learning
    Ramamonjisoa, David
    Ikuma, Hidernaru
    Murakami, Riki
    2022 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WI-IAT, 2022, : 335 - 340
  • [6] Exploration of social media for sentiment analysis using deep learning
    Chen, Liang-Chu
    Lee, Chia-Meng
    Chen, Mu-Yen
    SOFT COMPUTING, 2020, 24 (11) : 8187 - 8197
  • [7] Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response
    Alam, Firoj
    Ofli, Ferda
    Imran, Muhammad
    Alam, Tanvirul
    Qazi, Umair
    2020 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), 2020, : 151 - 158
  • [8] Automatic Recognition of Cyberbullying in the Web of Things and social media using Deep Learning Framework
    Al-Wesabi, Fahd N.
    Obayya, Marwa
    Alsamri, Jamal
    Alabdan, Rana
    Aljehane, Nojood O.
    Alazwari, Sana
    Alruwaili, Fahad F.
    Hamza, Manar Ahmed
    Swathi, S.
    IEEE TRANSACTIONS ON BIG DATA, 2025, 11 (01) : 259 - 270
  • [9] Personality Identification from Social Media Using Deep Learning: A Review
    Bhavya, S.
    Pillai, Anitha S.
    Guazzaroni, Giuliana
    SOFT COMPUTING FOR PROBLEM SOLVING, SOCPROS 2018, VOL 2, 2020, 1057 : 523 - 534
  • [10] Business intelligence using deep learning techniques for social media contents
    Tarek Kanan
    Ala Mughaid
    Riyad Al-Shalabi
    Mahmoud Al-Ayyoub
    Mohammed Elbes
    Odai Sadaqa
    Cluster Computing, 2023, 26 : 1285 - 1296